




'''simple docstring'''







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
    from fairscale.optim import OSS


lowerCAmelCase_     						=				logging.get_logger(__name__)

lowerCAmelCase_     						=				{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Optional[int]    ,		a       :  Dict=None    ,		a       :  int=None    ,		*a       :  Union[str, Any]    ,		**a       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    super().__init__(*a    ,		**a    )

    if config is None:
       assert isinstance(self.model    ,		a    ), (
           "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
           f""" {self.model.__class__}"""
       )
       A__       =     self.model.config
    else:
       A__       =     config

    A__       =     data_args
    A__       =     self.config.tgt_vocab_size if isinstance(self.config    ,		a    ) else self.config.vocab_size

    if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
       assert self.config.pad_token_id is not None, (
           "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
           " calculation or doing label smoothing."
       )

    if self.config.pad_token_id is None and self.config.eos_token_id is not None:
       logger.warning(
           f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
           ' padding..'    )

    if self.args.label_smoothing == 0:
       A__       =     torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id    )
    else:
       # dynamically import label_smoothed_nll_loss
       from utils import label_smoothed_nll_loss

       A__       =     label_smoothed_nll_loss






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   str:






    """simple docstring"""


    if self.optimizer is None:
       A__       =     ['bias', 'LayerNorm.weight']
       A__       =     [
           {
               'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay    )],
               'weight_decay': self.args.weight_decay,
           },
           {
               'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay    )],
               'weight_decay': 0.0,
           },
       ]
       A__       =     Adafactor if self.args.adafactor else AdamW
       if self.args.adafactor:
          A__       =     Adafactor
          A__       =     {'scale_parameter': False, 'relative_step': False}
       else:
          A__       =     AdamW
          A__       =     {
              'betas': (self.args.adam_betaa, self.args.adam_betaa),
              'eps': self.args.adam_epsilon,
          }
       A__       =     self.args.learning_rate
       if self.sharded_ddp:
          A__       =     OSS(
              params=a    ,		optim=a    ,		**a    ,		)
       else:
          A__       =     optimizer_cls(a    ,		**a    )

    if self.lr_scheduler is None:
       A__       =     self._get_lr_scheduler(a    )
    else:  # ignoring --lr_scheduler
       logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'    )






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  List[Any]    )       ->   str:






    """simple docstring"""


    A__       =     arg_to_scheduler[self.args.lr_scheduler]
    if self.args.lr_scheduler == "constant":
       A__       =     schedule_func(self.optimizer    )
    elif self.args.lr_scheduler == "constant_w_warmup":
       A__       =     schedule_func(self.optimizer    ,		num_warmup_steps=self.args.warmup_steps    )
    else:
       A__       =     schedule_func(
           self.optimizer    ,		num_warmup_steps=self.args.warmup_steps    ,		num_training_steps=a    )
    return scheduler






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Optional[torch.utils.data.Sampler]:






    """simple docstring"""


    if isinstance(self.train_dataset    ,		torch.utils.data.IterableDataset    ):
       return None
    elif is_torch_tpu_available():
       return get_tpu_sampler(self.train_dataset    )
    else:
       if self.args.sortish_sampler:
          self.train_dataset.make_sortish_sampler(
              self.args.per_device_train_batch_size    ,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,		)

       return (
           RandomSampler(self.train_dataset    )
           if self.args.local_rank == -1
           else DistributedSampler(self.train_dataset    )
       )






 def 						_UpperCamelCase (self       :  str    ,		a       :  List[Any]    ,		a       :  Optional[Any]    ,		a       :  Any    )       ->   str:






    """simple docstring"""


    if self.args.label_smoothing == 0:
       if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
          # force training to ignore pad token
          A__       =     model(**a    ,		use_cache=a    )[0]
          A__       =     self.loss_fn(logits.view(-1    ,		logits.shape[-1]    )    ,		labels.view(-1    )    )
       else:
          # compute usual loss via models
          A__  ,			A__       =     model(**a    ,		labels=a    ,		use_cache=a    )[:2]
    else:
       # compute label smoothed loss
       A__       =     model(**a    ,		use_cache=a    )[0]
       A__       =     torch.nn.functional.log_softmax(a    ,		dim=-1    )
       A__  ,			A__       =     self.loss_fn(a    ,		a    ,		self.args.label_smoothing    ,		ignore_index=self.config.pad_token_id    )
    return loss, logits






 def 						_UpperCamelCase (self       :  int    ,		a       :  Union[str, Any]    ,		a       :  List[Any]    )       ->   int:






    """simple docstring"""


    A__       =     inputs.pop('labels'    )
    A__  ,			A__       =     self._compute_loss(a    ,		a    ,		a    )
    return loss






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  nn.Module    ,		a       :  Dict[str, Union[torch.Tensor, Any]]    ,		a       :  bool    ,		a       :  Optional[List[str]] = None    ,		)       ->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:






    """simple docstring"""


    A__       =     self._prepare_inputs(a    )

    A__       =     {
        'max_length': self.data_args.val_max_target_length
        if self.data_args is not None
        else self.config.max_length,
        'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
    }

    if self.args.predict_with_generate and not self.args.prediction_loss_only:
       A__       =     self.model.generate(
           inputs['input_ids']    ,		attention_mask=inputs['attention_mask']    ,		**a    ,		)
       # in case the batch is shorter than max length, the output should be padded
       if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
          A__       =     self._pad_tensors_to_max_len(a    ,		gen_kwargs['max_length']    )

    A__       =     inputs.pop('labels'    )
    with torch.no_grad():
       # compute loss on predict data
       A__  ,			A__       =     self._compute_loss(a    ,		a    ,		a    )

    A__       =     loss.mean().detach()
    if self.args.prediction_loss_only:
       return (loss, None, None)

    A__       =     generated_tokens if self.args.predict_with_generate else logits

    if labels.shape[-1] < gen_kwargs["max_length"]:
       A__       =     self._pad_tensors_to_max_len(a    ,		gen_kwargs['max_length']    )

    return (loss, logits, labels)






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Tuple    ,		a       :  str    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

    if pad_token_id is None:
       raise ValueError(
           'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
           f""" padded to `max_length`={max_length}"""    )

    A__       =     pad_token_id * torch.ones(
        (tensor.shape[0], max_length)    ,		dtype=tensor.dtype    ,		device=tensor.device    )
    A__       =     tensor
    return padded_tensor








'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


